{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/2"}],"enrichment":{"capability":"Integrates Celery distributed task execution with Apache Airflow, enabling workers to process DAG tasks across a cluster using Celery as the task queue backend.","skillfed_tags":["airflow-executor","task-distribution","celery-integration"],"use_cases":["Scale Airflow task execution across multiple machines by running workers on separate nodes and routing DAG tasks through Celery.","Process long-running or CPU-intensive tasks in parallel without blocking the Airflow scheduler.","Monitor distributed task execution and worker health using flower's web dashboard.","Run Airflow in a multi-tenant environment where different teams or projects need isolated worker pools.","Integrate Airflow with an existing Celery infrastructure already in use for other applications."],"what_it_does":"This is an Apache Airflow provider package that plugs Celery into Airflow as a distributed task executor. Instead of running all DAG tasks on a single Airflow scheduler, it routes them to remote Celery workers across a cluster, letting you scale horizontally by adding more worker nodes. The package wraps Celery's task queue mechanics and integrates them with Airflow's DAG scheduling, dependency tracking, and monitoring.\n\nYou install it alongside an existing Airflow setup, configure it to point to a Celery broker (typically Redis or RabbitMQ), and then set Airflow to use CeleryExecutor. Airflow will then push tasks to the broker, workers pull and execute them, and results flow back. The package includes integration with flower, Celery's monitoring tool, so you can observe worker status and task progress from a web UI.","worth_installing":"Yes, if you are running Apache Airflow and need to scale task execution beyond a single machine. The package is actively maintained, has no known vulnerabilities, and is production-stable. Install only if you have a Celery broker already running or planned; it is not useful without one. The low install friction and permissive Apache-2.0 license make it a straightforward addition to an existing Airflow deployment."},"id":"apache-airflow-providers-celery","links":{"html":"https://skillfed.io/packages/apache-airflow-providers-celery","md":"https://skillfed.io/packages/apache-airflow-providers-celery.md","pypi":"https://pypi.org/project/apache-airflow-providers-celery/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-08","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"apache-airflow-providers-celery","python_support":"supports_current","summary":"Provider package apache-airflow-providers-celery for Apache Airflow"},"popularity":{"monthly_downloads":1867146,"position":3478,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.23.1"}